It is highly desirable in terms of speed and computational costs to perform image indexing and retrieval in the compressed domain. The exponential growth of digital media on both the WWW [1] and home imaging equipment has prompted the development of faster, more accurate indexing algorithms. Successful techniques have the ability to summarise the features of an image into a relatively small key...

Storage and retrieval of images in such libraries has become a real demand in industrial, medical, and other applications. Content-based image indexing and retrieval (CBIR) is considered as a solution. In such systems, in the indexing algorithm, some features are extracted from every picture and stored as an index vector. We apply tamura texture features on digital images and compute the low or...

In this paper we combine image feature extraction with indexing techniques for eecient retrieval in large texture images databases. A 2D image signal is processed using a set of Gabor lters to derive a 120 component feature vector representing the image. The feature components are ordered based on the relative importance in characterizing a given texture pattern, and this facilitates the develo...

The indexing methods currently used for serial femtosecond crystallography were originally developed for experiments in which crystals are rotated in the X-ray beam, providing significant three-dimensional information. On the other hand, shots from both X-ray free-electron lasers and serial synchrotron crystallography experiments are still images, in which the few three-dimensional data availab...

introduction: content based image retrieval (cbir) is a method of image searching and retrieval in a  database. in medical applications, cbir is a tool used by physicians to compare the previous and current  medical images associated with patients pathological conditions. as the volume of pictorial information  stored in medical image databases is in progress, efficient image indexing and retri...

In this paper, a new approach for content-based image indexing and retrieval is presented. The proposed method is based on a combination of multiresolution analysis and color correlation histogram of the image. According to the new algorithm, the wavelet coefficients of the image are computed first using Daubechies3 wavelet. Then, monodimensional color correlogram of the horizontal and vertical...

We present the idea of peer indexing—indexing an image by semantically correlated images—and its application in image retrieval. A learning strategy is suggested for automatic acquisition of peer indices from user feedbacks, and the similarity metric for peer index is formulated. A cooperative framework is proposed under which peer index is integrated with low-level features for image retrieval...

In this paper, a novel approach to image indexing is presented using content-based watermarking. Some concepts associated with the application of watermarking to image indexing are discussed and a segmentation algorithm, appropriate for content-based watermarking, is presented. The segmentation algorithm is applied on reduced images and derives the exact same objects when performed on either th...

It is advantageous in terms computational efficiency to carry out image indexing and retrieval based on compressed data. We describe an image indexing and retrieval scheme based on vector quantization (VQ). Experimental results show that the proposed scheme has higher retrieval performance than the basic colour histogram based method.

This paper introduces a novel indexing and access method, called FeatureBased Adaptive Tolerance Tree (FATT), using wavelet transform is proposed to organize large image data sets efficiently and to support popular image access mechanisms like Content Based Image Retrieval (CBIR).Conventional database systems are designed for managing textual and numerical data and retrieving such data is often...